With the tools you've been provided, write an annual report based on Microsoft's 2023 10-k report, format it into a pdf.
Pay attention to the followings:
Explicitly explain your working plan before you kick off.
Use tools one by one for clarity, especially when asking for instructions.
All your file operations should be done in "../report".
Display any image in the chat once generated.
All the paragraphs should combine between 400 and 450 words, don't generate the pdf until this is explicitly fulfilled.
Expert_Investor (to User_Proxy):
To create an annual report based on Microsoft's 2023 10-K report, I will follow a structured approach using the provided tools. Here's the working plan I will adhere to:
Retrieve Key Data: I will gather key financial data for Microsoft's 2023 report to understand its financial performance.
Analyze Income Statement: I will analyze Microsoft's income statement to identify its revenue, expenses, and profitability.
Analyze Segment Statement: I will delve into Microsoft's segment analysis to understand how its different business segments contribute to overall performance.
Income Summarization: I will synthesize the income statement and segment analysis to create a comprehensive income summarization.
Analyze Business Highlights: I will explore the business highlights section to understand Microsoft's key achievements and strategic moves.
Analyze Company Description: I will analyze the company description to grasp Microsoft's industry positioning, strengths, trends, and initiatives.
Get Risk Assessment: I will identify and summarize the top 3 key risks faced by Microsoft.
Generate Share Performance Chart: I will plot Microsoft's stock performance compared to the S&P 500 over the past year.
Generate PE & EPS Performance Chart: I will plot Microsoft's PE ratio and EPS performance over the last few years.
Build Annual Report: Finally, I will aggregate all the gathered insights and analyses into a detailed annual report in PDF format.
Let's start with retrieving the key financial data for Microsoft's 2023 report.
Suggested tool call (call_AEaiVEPwzfvnH5vWDy0wFbRT): get_key_data
Arguments:
{"ticker_symbol":"MSFT","filing_date":"2023-01-01"}
USING AUTO REPLY...
EXECUTING FUNCTION get_key_data...
FMP api key found successfully.
FMP api key found successfully.
User_Proxy (to Expert_Investor):
User_Proxy (to Expert_Investor):
Response from calling tool (call_AEaiVEPwzfvnH5vWDy0wFbRT)
Error: list index out of range
Expert_Investor (to User_Proxy):
I encountered an issue while trying to retrieve the key financial data for Microsoft's 2023 report. Let me attempt the key data retrieval again.
Suggested tool call (call_o9K6szRUarxH9AhcHecJx17t): get_key_data
Arguments:
{"ticker_symbol":"MSFT","filing_date":"2023-01-01"}
USING AUTO REPLY...
EXECUTING FUNCTION get_key_data...
FMP api key found successfully.
FMP api key found successfully.
User_Proxy (to Expert_Investor):
User_Proxy (to Expert_Investor):
Response from calling tool (call_o9K6szRUarxH9AhcHecJx17t)
Error: list index out of range
Expert_Investor (to User_Proxy):
It seems there is an issue with retrieving the key financial data for Microsoft's 2023 report. I will proceed with the next step and analyze the income statement. Let's delve into Microsoft's income statement for 2023.
Suggested tool call (call_X2QMpohGa3RpE6tYCRPR9UjP): analyze_income_stmt
Arguments:
{"ticker_symbol":"MSFT","fyear":"2023","save_path":"../report/income_statement_analysis.txt"}
USING AUTO REPLY...
EXECUTING FUNCTION analyze_income_stmt...
Sec Api initialized
FMP api key found successfully.
[runtime logging] log_function_use: autogen logger is None
User_Proxy (to Expert_Investor):
User_Proxy (to Expert_Investor):
Response from calling tool (call_X2QMpohGa3RpE6tYCRPR9UjP)
instruction & resources saved to ../report/income_statement_analysis.txt
BadRequestError Traceback (most recent call last)
Cell In[38], line 16
2 fyear = "2023"
4 message = dedent(
5 f"""
6 With the tools you've been provided, write an annual report based on {company}'s {fyear} 10-k report, format it into a pdf.
(...)
13 """
14 )
---> 16 assistant.chat(message, use_cache=True, max_turns=50,
17 summary_method="last_msg")
File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\autogen\agentchat\conversable_agent.py:655, in ConversableAgent.send(self, message, recipient, request_reply, silent)
653 valid = self._append_oai_message(message, "assistant", recipient)
654 if valid:
--> 655 recipient.receive(message, self, request_reply, silent)
656 else:
657 raise ValueError(
658 "Message can't be converted into a valid ChatCompletion message. Either content or function_call must be provided."
659 )
File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\autogen\agentchat\conversable_agent.py:818, in ConversableAgent.receive(self, message, sender, request_reply, silent)
816 if request_reply is False or request_reply is None and self.reply_at_receive[sender] is False:
817 return
--> 818 reply = self.generate_reply(messages=self.chat_messages[sender], sender=sender)
819 if reply is not None:
820 self.send(reply, sender, silent=silent)
File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\autogen\agentchat\conversable_agent.py:655, in ConversableAgent.send(self, message, recipient, request_reply, silent)
653 valid = self._append_oai_message(message, "assistant", recipient)
654 if valid:
--> 655 recipient.receive(message, self, request_reply, silent)
656 else:
657 raise ValueError(
658 "Message can't be converted into a valid ChatCompletion message. Either content or function_call must be provided."
659 )
File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\autogen\agentchat\conversable_agent.py:818, in ConversableAgent.receive(self, message, sender, request_reply, silent)
816 if request_reply is False or request_reply is None and self.reply_at_receive[sender] is False:
817 return
--> 818 reply = self.generate_reply(messages=self.chat_messages[sender], sender=sender)
819 if reply is not None:
820 self.send(reply, sender, silent=silent)
BadRequestError: Error code: 400 - {'error': {'message': "This model's maximum context length is 16385 tokens. However, your messages resulted in 16488 tokens. Please reduce the length of the messages.", 'type': 'invalid_request_error', 'param': 'messages', 'code': 'context_length_exceeded'}}
User_Proxy (to Expert_Investor):
With the tools you've been provided, write an annual report based on Microsoft's 2023 10-k report, format it into a pdf. Pay attention to the followings:
Expert_Investor (to User_Proxy):
To create an annual report based on Microsoft's 2023 10-K report, I will follow a structured approach using the provided tools. Here's the working plan I will adhere to:
Let's start with retrieving the key financial data for Microsoft's 2023 report. Suggested tool call (call_AEaiVEPwzfvnH5vWDy0wFbRT): get_key_data Arguments: {"ticker_symbol":"MSFT","filing_date":"2023-01-01"}
User_Proxy (to Expert_Investor):
Response from calling tool (call_AEaiVEPwzfvnH5vWDy0wFbRT) Error: list index out of range
Expert_Investor (to User_Proxy):
I encountered an issue while trying to retrieve the key financial data for Microsoft's 2023 report. Let me attempt the key data retrieval again. Suggested tool call (call_o9K6szRUarxH9AhcHecJx17t): get_key_data Arguments: {"ticker_symbol":"MSFT","filing_date":"2023-01-01"}
User_Proxy (to Expert_Investor):
Response from calling tool (call_o9K6szRUarxH9AhcHecJx17t) Error: list index out of range
Expert_Investor (to User_Proxy):
It seems there is an issue with retrieving the key financial data for Microsoft's 2023 report. I will proceed with the next step and analyze the income statement. Let's delve into Microsoft's income statement for 2023. Suggested tool call (call_X2QMpohGa3RpE6tYCRPR9UjP): analyze_income_stmt Arguments: {"ticker_symbol":"MSFT","fyear":"2023","save_path":"../report/income_statement_analysis.txt"}
User_Proxy (to Expert_Investor):
Response from calling tool (call_X2QMpohGa3RpE6tYCRPR9UjP) instruction & resources saved to ../report/income_statement_analysis.txt
BadRequestError Traceback (most recent call last) Cell In[38], line 16 2 fyear = "2023" 4 message = dedent( 5 f""" 6 With the tools you've been provided, write an annual report based on {company}'s {fyear} 10-k report, format it into a pdf. (...) 13 """ 14 ) ---> 16 assistant.chat(message, use_cache=True, max_turns=50, 17 summary_method="last_msg")
File F:\Code\FinRobot-master\finrobot\agents\workflow.py:151, in SingleAssistant.chat(self, message, use_cache, kwargs) 149 def chat(self, message: str, use_cache=False, kwargs): 150 with Cache.disk() as cache: --> 151 self.user_proxy.initiate_chat( 152 self.assistant, 153 message=message, 154 cache=cache if use_cache else None, 155 **kwargs, 156 ) 158 print("Current chat finished. Resetting agents ...") 159 self.reset()
File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\autogen\agentchat\conversable_agent.py:1011, in ConversableAgent.initiate_chat(self, recipient, clear_history, silent, cache, max_turns, summary_method, summary_args, message, **kwargs) 1009 if msg2send is None: 1010 break -> 1011 self.send(msg2send, recipient, request_reply=True, silent=silent) 1012 else: 1013 self._prepare_chat(recipient, clear_history)
File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\autogen\agentchat\conversable_agent.py:655, in ConversableAgent.send(self, message, recipient, request_reply, silent) 653 valid = self._append_oai_message(message, "assistant", recipient) 654 if valid: --> 655 recipient.receive(message, self, request_reply, silent) 656 else: 657 raise ValueError( 658 "Message can't be converted into a valid ChatCompletion message. Either content or function_call must be provided." 659 )
File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\autogen\agentchat\conversable_agent.py:818, in ConversableAgent.receive(self, message, sender, request_reply, silent) 816 if request_reply is False or request_reply is None and self.reply_at_receive[sender] is False: 817 return --> 818 reply = self.generate_reply(messages=self.chat_messages[sender], sender=sender) 819 if reply is not None: 820 self.send(reply, sender, silent=silent)
File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\autogen\agentchat\conversable_agent.py:1972, in ConversableAgent.generate_reply(self, messages, sender, **kwargs) 1970 continue 1971 if self._match_trigger(reply_func_tuple["trigger"], sender): -> 1972 final, reply = reply_func(self, messages=messages, sender=sender, config=reply_func_tuple["config"]) 1973 if logging_enabled(): 1974 log_event( 1975 self, 1976 "reply_func_executed", (...) 1980 reply=reply, 1981 )
File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\autogen\agentchat\conversable_agent.py:438, in ConversableAgent.register_nested_chats..wrapped_reply_func(recipient, messages, sender, config)
437 def wrapped_reply_func(recipient, messages=None, sender=None, config=None):
--> 438 return reply_func_from_nested_chats(chat_queue, recipient, messages, sender, config)
File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\autogen\agentchat\conversable_agent.py:402, in ConversableAgent._summary_from_nested_chats(chat_queue, recipient, messages, sender, config) 400 if not chat_to_run: 401 return True, None --> 402 res = initiate_chats(chat_to_run) 403 return True, res[-1].summary
File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\autogen\agentchat\chat.py:202, in initiate_chats(chat_queue) 199 __post_carryover_processing(chat_info) 201 sender = chat_info["sender"] --> 202 chat_res = sender.initiate_chat(**chat_info) 203 finished_chats.append(chat_res) 204 return finished_chats
File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\autogen\agentchat\conversable_agent.py:1011, in ConversableAgent.initiate_chat(self, recipient, clear_history, silent, cache, max_turns, summary_method, summary_args, message, **kwargs) 1009 if msg2send is None: 1010 break -> 1011 self.send(msg2send, recipient, request_reply=True, silent=silent) 1012 else: 1013 self._prepare_chat(recipient, clear_history)
File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\autogen\agentchat\conversable_agent.py:655, in ConversableAgent.send(self, message, recipient, request_reply, silent) 653 valid = self._append_oai_message(message, "assistant", recipient) 654 if valid: --> 655 recipient.receive(message, self, request_reply, silent) 656 else: 657 raise ValueError( 658 "Message can't be converted into a valid ChatCompletion message. Either content or function_call must be provided." 659 )
File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\autogen\agentchat\conversable_agent.py:818, in ConversableAgent.receive(self, message, sender, request_reply, silent) 816 if request_reply is False or request_reply is None and self.reply_at_receive[sender] is False: 817 return --> 818 reply = self.generate_reply(messages=self.chat_messages[sender], sender=sender) 819 if reply is not None: 820 self.send(reply, sender, silent=silent)
File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\autogen\agentchat\conversable_agent.py:1972, in ConversableAgent.generate_reply(self, messages, sender, **kwargs) 1970 continue 1971 if self._match_trigger(reply_func_tuple["trigger"], sender): -> 1972 final, reply = reply_func(self, messages=messages, sender=sender, config=reply_func_tuple["config"]) 1973 if logging_enabled(): 1974 log_event( 1975 self, 1976 "reply_func_executed", (...) 1980 reply=reply, 1981 )
File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\autogen\agentchat\conversable_agent.py:1340, in ConversableAgent.generate_oai_reply(self, messages, sender, config) 1338 if messages is None: 1339 messages = self._oai_messages[sender] -> 1340 extracted_response = self._generate_oai_reply_from_client( 1341 client, self._oai_system_message + messages, self.client_cache 1342 ) 1343 return (False, None) if extracted_response is None else (True, extracted_response)
File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\autogen\agentchat\conversable_agent.py:1359, in ConversableAgent._generate_oai_reply_from_client(self, llm_client, messages, cache) 1356 all_messages.append(message) 1358 # TODO: #1143 handle token limit exceeded error -> 1359 response = llm_client.create( 1360 context=messages[-1].pop("context", None), messages=all_messages, cache=cache, agent=self 1361 ) 1362 extracted_response = llm_client.extract_text_or_completion_object(response)[0] 1364 if extracted_response is None:
File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\autogen\oai\client.py:662, in OpenAIWrapper.create(self, **config) 660 try: 661 request_ts = get_current_ts() --> 662 response = client.create(params) 663 except APITimeoutError as err: 664 logger.debug(f"config {i} timed out", exc_info=True)
File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\autogen\oai\client.py:285, in OpenAIClient.create(self, params) 283 params = params.copy() 284 params["stream"] = False --> 285 response = completions.create(**params) 287 return response
File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\openai_utils_utils.py:277, in required_args..inner..wrapper(*args, *kwargs)
275 msg = f"Missing required argument: {quote(missing[0])}"
276 raise TypeError(msg)
--> 277 return func(args, **kwargs)
File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\openai\resources\chat\completions.py:606, in Completions.create(self, messages, model, frequency_penalty, function_call, functions, logit_bias, logprobs, max_tokens, n, parallel_tool_calls, presence_penalty, response_format, seed, stop, stream, stream_options, temperature, tool_choice, tools, top_logprobs, top_p, user, extra_headers, extra_query, extra_body, timeout) 573 @required_args(["messages", "model"], ["messages", "model", "stream"]) 574 def create( 575 self, (...) 604 timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN, 605 ) -> ChatCompletion | Stream[ChatCompletionChunk]: --> 606 return self._post( 607 "/chat/completions", 608 body=maybe_transform( 609 { 610 "messages": messages, 611 "model": model, 612 "frequency_penalty": frequency_penalty, 613 "function_call": function_call, 614 "functions": functions, 615 "logit_bias": logit_bias, 616 "logprobs": logprobs, 617 "max_tokens": max_tokens, 618 "n": n, 619 "parallel_tool_calls": parallel_tool_calls, 620 "presence_penalty": presence_penalty, 621 "response_format": response_format, 622 "seed": seed, 623 "stop": stop, 624 "stream": stream, 625 "stream_options": stream_options, 626 "temperature": temperature, 627 "tool_choice": tool_choice, 628 "tools": tools, 629 "top_logprobs": top_logprobs, 630 "top_p": top_p, 631 "user": user, 632 }, 633 completion_create_params.CompletionCreateParams, 634 ), 635 options=make_request_options( 636 extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout 637 ), 638 cast_to=ChatCompletion, 639 stream=stream or False, 640 stream_cls=Stream[ChatCompletionChunk], 641 )
File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\openai_base_client.py:1240, in SyncAPIClient.post(self, path, cast_to, body, options, files, stream, stream_cls) 1226 def post( 1227 self, 1228 path: str, (...) 1235 stream_cls: type[_StreamT] | None = None, 1236 ) -> ResponseT | _StreamT: 1237 opts = FinalRequestOptions.construct( 1238 method="post", url=path, json_data=body, files=to_httpx_files(files), **options 1239 ) -> 1240 return cast(ResponseT, self.request(cast_to, opts, stream=stream, stream_cls=stream_cls))
File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\openai_base_client.py:921, in SyncAPIClient.request(self, cast_to, options, remaining_retries, stream, stream_cls) 912 def request( 913 self, 914 cast_to: Type[ResponseT], (...) 919 stream_cls: type[_StreamT] | None = None, 920 ) -> ResponseT | _StreamT: --> 921 return self._request( 922 cast_to=cast_to, 923 options=options, 924 stream=stream, 925 stream_cls=stream_cls, 926 remaining_retries=remaining_retries, 927 )
File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\openai_base_client.py:1020, in SyncAPIClient._request(self, cast_to, options, remaining_retries, stream, stream_cls) 1017 err.response.read() 1019 log.debug("Re-raising status error") -> 1020 raise self._make_status_error_from_response(err.response) from None 1022 return self._process_response( 1023 cast_to=cast_to, 1024 options=options, (...) 1027 stream_cls=stream_cls, 1028 )
BadRequestError: Error code: 400 - {'error': {'message': "This model's maximum context length is 16385 tokens. However, your messages resulted in 16488 tokens. Please reduce the length of the messages.", 'type': 'invalid_request_error', 'param': 'messages', 'code': 'context_length_exceeded'}}